Join Prior Labs Prior Labs is building breakthrough foundation models that understand spreadsheets and databases - the backbone of science and business. Foundation models have transformed text and images, but structured data has remained largely untouched. We're tackling this $100B opportunity to revolutionize how we approach scientific discovery, medical research, financial modeling, and business intelligence. We aim to be the world leading organization working on structured data. Our TabPFN v2 model, was just published in the Nature Journal and is the new state-of-the-art for small structured data. Our models have significant traction (900K downloads, 2,000 GitHub stars) and we are now building the next generation of models that combine AI advances with specialized architectures for structured data. With backing from top-tier investors and some of the top AI leaders, we're rapidly moving toward commercialization. Want to help organizations worldwide unlock the true value of their most critical data assets? Join us in making it happen. Core Areas of Impact As an ML Engineer, Systems & Evaluation, you'll play a critical role in ensuring the reliability, efficiency, and performance of our models at scale. You'll work on designing robust evaluation systems, optimizing model performance, and ensuring our infrastructure can support cutting-edge AI development and deployment. You'll also lead initiatives around data collection, benchmarking, and systematic evaluation to drive continuous model improvement. This is a rare opportunity to: Contribute to the development of high-impact AI systems Design and implement large-scale model evaluation pipelines Lead data collection and benchmarking efforts to measure model performance Drive continuous performance improvements through rigorous testing Join at the perfect time: Significant funding secured, strong early traction, and rapid scaling Key Responsibilities Systems Engineering Design, implement, and maintain scalable infrastructure to support large-scale model training and deployment. Model Evaluation & Benchmarking Develop comprehensive evaluation frameworks to assess model performance, robustness, and reliability. Design and maintain benchmarking protocols to compare models against industry standards. Data Collection & Curation Lead efforts to collect, curate, and manage high-quality datasets essential for training and evaluating models. Performance Optimization Identify bottlenecks and optimize training and inference pipelines for efficiency and speed. Monitoring & Reliability Build monitoring systems to ensure the health and reliability of deployed models. Cross-functional Collaboration Work closely with ML researchers, product managers, and data engineers to align system architecture with research goals. What We're Looking For Strong engineering fundamentals with excellent Python skills Experience with ML frameworks, especially PyTorch, Scikit-learn Proven track record of building scalable ML systems Experience with distributed computing and performance optimization Strong problem-solving skills with a systems-thinking mindset Passion for building clean, maintainable, and well-documented code What Sets You Apart Experience with ML systems design, benchmarking, and evaluation frameworks Expertise in data collection, dataset curation, and data quality management Contributions to open-source projects in ML systems or infrastructure Expertise in distributed training, containerization (Docker, Kubernetes), and cloud platforms (AWS, GCP, Azure) Strong background in performance profiling, optimization, and monitoring Knowledge of MLOps best practices and CI/CD pipelines We're at the forefront of bringing foundation model breakthroughs to tabular data, with the potential to transform how organizations worldwide make decisions. If you're excited about creating robust, scalable systems that will power the next generation of AI applications, we'd love to hear from you.